The prediction of human shifts of attention is a widely-studied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer’s goals, has received far less attention, even though visual search constitutes much of a person’s everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 images were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results of several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.
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Benchmarking Gaze Prediction for Categorical Visual Search
The prediction of human shifts of attention is a widely-studied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer's goals, has received far less attention, even though visual search constitutes much of a person's everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 images were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results of several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.
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- Award ID(s):
- 1763981
- NSF-PAR ID:
- 10091972
- Date Published:
- Journal Name:
- CVPR Workshop - Mutual Benefits of Cognitive and Computer Vision
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer’s internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of highquality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive targetdependent patterns of object prioritization, which we interpret as a learned object context.more » « less
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Abstract Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control – saliency. We introduce COCO-Search18, the first dataset of laboratory-quality
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Human efficiency in finding a target in an image has attracted the attention of machine learning researchers, but what about when no target is there? Knowing how people search in the absence of a target, and when they stop, is important for Human-computer-interaction systems attempting to predict human gaze behavior in the wild. Here we report a rigorous evaluation of target-absent search behavior using the COCO-Search18 dataset to train stateof- the-art models. We focus on two specific aims. First, we characterize the presence of a target guidance signal in target-absent search behavior by comparing it to targetpresent guidance and free viewing. We do this by comparing how well a model trained on one type of fixation behavior (target-present, target-absent, free viewing) can predict behavior in either the same or different task. To compare target-absent search to free viewing behavior we created COCO-FreeView, a dataset of free-viewing fixations for the same images used in COCO-Search18. These comparisons revealed the existence of a target guidance signal in targetabsent search, albeit one much less dominant compared to when a target actually appeared in an image, and that the target-absent guidance signal was similar to free viewing in that saliency and center bias were both weighted more than guidance from target features. Our second aim focused on the stopping criteria, a question intrinsic to target-absent search. Here we propose to train a foveated target detector whose target detection representation is sensitive to the relationship between distance from the fovea. Then combining the predicted target detection representation with other information such as fixation history and subject ID, our model outperforms the baselines in predicting when a person stops moving his attention during target-absent search.more » « less
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Abstract Previous work has demonstrated similarities and differences between aerial and terrestrial image viewing. Aerial scene categorization, a pivotal visual processing task for gathering geoinformation, heavily depends on rotation-invariant information. Aerial image-centered research has revealed effects of low-level features on performance of various aerial image interpretation tasks. However, there are fewer studies of viewing behavior for aerial scene categorization and of higher-level factors that might influence that categorization. In this paper, experienced subjects’ eye movements were recorded while they were asked to categorize aerial scenes. A typical viewing center bias was observed. Eye movement patterns varied among categories. We explored the relationship of nine image statistics to observers’ eye movements. Results showed that if the images were less homogeneous, and/or if they contained fewer or no salient diagnostic objects, viewing behavior became more exploratory. Higher- and object-level image statistics were predictive at both the image and scene category levels. Scanpaths were generally organized and small differences in scanpath randomness could be roughly captured by critical object saliency. Participants tended to fixate on critical objects. Image statistics included in this study showed rotational invariance. The results supported our hypothesis that the availability of diagnostic objects strongly influences eye movements in this task. In addition, this study provides supporting evidence for Loschky et al.’s (Journal of Vision, 15(6), 11, 2015) speculation that aerial scenes are categorized on the basis of image parts and individual objects. The findings were discussed in relation to theories of scene perception and their implications for automation development.
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Accepted Manuscript1.0
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